Package: blockForest
Type: Package
Title: Block Forests: Random Forests for Blocks of Clinical and Omics
        Covariate Data
Version: 0.1.7
Date: 2018-12-18
Author: Roman Hornung, Marvin N. Wright
Maintainer: Marvin N. Wright <cran@wrig.de>
Description: A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival 
    and continuous outcomes using block-structured covariate data, for example, clinical covariates plus 
    measurements of a certain omics data type or multi-omics data, that is, data for which measurements of 
    different types of omics data and/or clinical data for each patient exist. Examples of different omics 
    data types include gene expression measurements, mutation data and copy number variation measurements. 
    Block forest are presented in Hornung & Wright (2018). The package includes four other random forest 
    variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were 
    also considered in Hornung & Wright (2018), but performed worse than block forest in their comparison 
    study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') 
    in applications. The other random forest variants can, however, be consulted for academic purposes, 
    for example, in the context of further methodological developments. 
    Reference: Hornung, R. & Wright, M. N. (2018) Block Forests: random forests for blocks of clinical and 
    omics covariate data. Technical Report, Department of Statistics, University of Munich.
License: GPL-3
Imports: Rcpp (>= 0.11.2), Matrix, methods, survival
LinkingTo: Rcpp, RcppEigen
Depends: R (>= 3.1)
Suggests: testthat
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2018-12-18 11:41:08 UTC; wright
Repository: CRAN
Date/Publication: 2018-12-30 17:20:12 UTC
